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Found 7 papers, 4 papers with code
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor.
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss.
Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
To achieve high-quality results, we design a multi-task architecture of diffusion model that strategically generates aspects of human motions for local guidance.
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Generating realistic motions for digital humans is time-consuming for many graphics applications.
CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy
Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment.
Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video
To this end, we propose to classify Parkinson's tremor since it is one of the most predominant symptoms of PD with strong generalizability.
Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
Our prediction performance achieves state-of-the-art research on both datasets.
